lecture 8: protein modeling · •case study –modelling the drug binding site of herg. why...
TRANSCRIPT
1
Lecture 8: Protein Modeling
Junmei Wang
Department of Pharmacology, University of Texas
Southwestern Medical Center at Dallas
2
Project 2: MD Simulations
1. Select a protein system
Protein Class PDB Code -logkd Resolution
Neuraminidase 2QWG 8.4 1.8
DHFR 1DHF 7.4 2.3
L-arabinose 1ABE 6.52 1.7
Thrombin 1A5G 10.15 2.06
Human oxresin receptor 1 4ZJ8 ~10 2.75
3
Project 2 MD Simulations -Continued
2. Select top hits from autodock-vina screening • Top 2 and bottom 1
3. Prepare ligand structures and residue topologies• Add hydrogen with adt• Generate Gaussian gcrt file with antechamber• Run G09 to calculate electrostatic potentials (ESP)• Run Antechamber to assign RESP charges• An alternative is run antechamber to assign am1-
bcc charge
4
Project 2 MD Simulations -Continued
4. Prepare topology files for minimization and MD simulations• xleap• tleap
5. Run Minimization and MD simulations using a delicate scheme• Minimization with main chain restrained using a set
of gradually reduced restraint force constants• MD simulation with main chain restrained using a
set of gradually reduced restraint force constants• Heat systems up using a set of temperatures• Equilibrium phase• Sampling phase
5
Project 2 MD Simulations -Continued
6. Analyze MD snapshots• Average structure• RMSD ~ simulation time plots • Quasi-harmonic analysis • MD movie
6
Project 3: Binding Free Energy Calculations With MM-PB/GBSA
1. Select a protein system
Protein Class PDB Code -logkd Resolution
Neuraminidase 2QWG 8.4 1.8
DHFR 1DHF 7.4 2.3
L-arabinose 1ABE 6.52 1.7
Thrombin 1A5G 10.15 2.06
Human oxresin receptor 1 4ZJ8 ~10 2.75
7
Project 3: Binding Free Energy Calculations With MM-PB/GBSA
2. Prepare topologies for energy calculations with implicit solvent• Xleap• Tleap
3. Run mmpbsa.py to do the calculation• Input file• Output files
4. Analyze the MM-PB/GBSA results
Protein Modeling
Contents
• Introduce the process of homology modelling.
• Summarise the methods for predicting the structure from sequence.
• Describe the individual steps involved in creating and optimising a protein homology model.
• Outline the methods available to evaluate the quality of homology models.
• Case Study – Modelling the Drug binding site of hERG.
Why Homology Model?
• Solving protein structures is not trivial.
• There are currently ~1.8 million known protein coding sequences.
• But only ~120,000 protein structures in the PDB.
• Even so, many of these structures are duplicates.
• For Membrane Proteins structural data is even more sparse:
• There are currently 2829 membrane protein structures
RSCB Protein Data Bank (PDB)
Statistics (22/07/16)
Method Totals
X-ray 117790
NMR 11469
EM 1089
Other 198
Total 120642
www.rscb.org
Amino Acid Residues
• Proteins are made up of amino acids, which are interconnected by peptide bonds.
• There are 20 naturally occurring amino acids.
• Amino acids may be subdivided by their individual properties.
DSSRRQYQEKYKQVEQYMSFHKLPADFRQKIHDYYEHRYQGKMFDEDSILGELNGPLREEIVNFNCR
KLVASMPLFANADPNFVTAMLTKLKFEVFQPGDYIIREGTIGKKMYFIQHGVVSVLTGNKEMKLSDG
SYFGEICLLTRGRRTASVRADTYCRLYSLSVDNFNEVLEEYPMMRRAFETYVAIDRLDRIGKKNSIL
From Sequence to Structure
SecondaryStructure
TertiaryStructure
QuaternaryStructure
Primary Structure – Amino Acid Sequence
What information can we get from a Sequence of amino acids?
Secondary Structure Prediction
• The Secondary Structure of Proteins is Defined by the DSSP algorithm.
• Amino acids classified as either α-helix (H), β-strand (S) or loop (C).
• It is possible to extract structural information from amino acid sequence.
• These prediction methods were initially proposed by Chou & Fasman in 1978.
• They used a statistical method based on 15 known crystal structures.
• Recent developments and an increase in structural information has improved these methods and they are currently ~80% accurate.
PSI-Pred: http://bioinf.cs.ucl.ac.uk/psipred/
JPred: http://www.compbio.dundee.ac.uk/~www-jpred/
Transmembrane Helix Prediction
• The amino acids at the centre of transmembrane helices are generally hydrophobic in nature.
• Analysis of Hydropathicity can be used to predict the number of membrane spanning helices.
• The analysis for the G-protein coupled receptor to the right suggests it has 7 TM helices.
• The example used the Kyte & Doolittle scale.
Hydropathy Plot
http://expasy.org/tools/protscale.html
BLAST
• How to find an appropriate template Structure for homology modelling…
• Basic Local Alignment Search Tool
• Used to search protein databases:
• e.g. Non-redundant (nr) & SwissProtto find similar sequences.
• Protein Data Bank (PDB) to find structures with similar sequences.
• PSI- & PHI-blast are more advanced Blast methods.
http://www.ncbi.nlm.nih.gov/blast/Blast.cgi
The Importance of Resolution
• In X-ray crystallography it is not always possible to flawlessly resolve the crystal density of the protein of interest.
• This results in a lower resolution structure.
• The lower the resolution the more likely the structure is wrong.
• The resolution of the template structure also reflects in the quality of the homology model.
high
low4 Å
2 Å
3 Å
1 Å
Sequence Alignment
Aligns the sequence(s) of interest to that of the template structure(s):
• Emboss may be used for two sequence, to generate a pairwise alignment & a percentage identity – ideally an identity of >50%:
http://www.ebi.ac.uk/emboss/align/
• T-Coffee, Clustal & MUSCLE are popular methods for multiple sequence alignment. All may be found at :
http://www.ebi.ac.uk/
• ESPRIPT is useful for formatting to creating black & white figures:
http://espript.ibcp.fr/
• Promals3d: Nick Grishin at UTSW
http://prodata.swmed.edu/promals3d/promals3d.php
Automated Homology Modelling
If you are lazy there are servers that do the modelling for you!
• Swiss Model : http://swissmodel.expasy.org//SWISS-MODEL.html
• Robetta : http://robetta.bakerlab.org/
• 3D Jigsaw : http://www.bmm.icnet.uk/servers/3djigsaw/
• Phyre : http://www.sbg.bio.ic.ac.uk/phyre/
• EsyPred3D : http://www.fundp.ac.be/sciences/biologie/urbm/bioinfo/esypred/
• CPHmodels : http://www.cbs.dtu.dk/services/CPHmodels/
Eva-CM performs continuous and automated analysis of comparative protein structure modeling servershttp://pdg.cnb.uam.es/eva/doc/intro_cm.html
Modeller
from modeller import *
from modeller.automodel import *
log.verbose()
env = environ()
env.io.atom_files_directory = './'
a = automodel(
env,
alnfile = 'herg.ali',
knowns = '1q5o',
sequence = 'herg'
)
a.starting_model= 1
a.ending_model = 1
a.make()
>P1;1q5o
structureX: 1q5o : 443 : A : 644 : A ::::
DSSRRQYQEKYKQVEQYMSFHKLPADFRQKIHDYYEHRYQ-GKMFDEDSILGELNGPLRE
EIVNFNCRKLVASMPLFANADPNFVTAMLTKLKFEVFQPGDYIIREGTIGKKMYFIQHGV
VSVLTKGNKEMKLSDGSYFGEICLL--TRGRRTASVRADTYCRLYSLSVDNFNEVLEEYP
MMRRAFETVAIDRLDRIGKKNSIL.*
>P1;herg
sequence: herg : 1 :::::::
YSGTARYHTQMLRVREFIRFHQIPNPLRQRLEEYFQHAWSYTNGIDMNAVLKGFPECLQA
DICLHLNRSLLQHCKPFRGATKGCLRALAMKFKTTHAPPGDTLVHAGDLLTALYFISRGS
IEILRGDVVVAILGKNDIFGEPLNLYARPGKSNGDVRALTYCDLHKIHRDDLLEVLDMYP
EFSDHFWSSLEITFNLRDTN-MIP.*
• Well regarded program for Homology/Comparative Modelling.
• Current Version 9.17 https://salilab.org/modeller/
• Requires an Input file, Sequence alignment & Template structure.
ATOM 1 N ASP A 443 -15.943 41.425 44.702 1.00 44.68
ATOM 2 CA ASP A 443 -15.424 42.618 45.447 1.00 43.15
ATOM 3 C ASP A 443 -14.310 43.306 44.686 1.00 41.81
ATOM 4 O ASP A 443 -14.298 44.528 44.539 1.00 42.61
etc...
Input File (*.py) Template Structure (*.pdb)
Sequence Alignment (*.ali)
How Does it Work?
EnergyMinimisation
Amino acid Substitution
Template Structure Initial Model (*.ini) Output Model(s) (*.B999*)
Valine Glutamine Change inRotamer
Modeller : Output
• .log : log output from the run.
• .B* : model generated in the PDB format.
• .D* : progress of optimisation.
• .V* : violation profile.
• .ini : initial model that is generated.
• .rsr : restraints in user format.
• .sch : schedule file for the optimisation process.
Modeller Features & Restraints
• Secondary Structure.Regions of the protein may be forced to be α-helical or β-strand.
• Distance restraints.The distance between atoms may be restrained.
• Symmetry.Protein multimers can be restrained so that all monomers are identical.
• Disulphide Bridges.Two cysteine residues in the model can be forced to make a cystine bond.
• Ligands.Ions, waters and small molecules may be included from the template.
• Loop Refinement.Regions without secondary structure often require further refinement.
An Iterative Process
Structural Convergence
• The catalytic triad of Serine, Aspartate and Histidine is found in certain protease enzymes. (a) Subtilisin (b) Chymotrypsin.
• However, the overall structure of the enzyme is often different.
• This is also important when considering ligand binding sites.
Modelling Ligand Interactions
• Small molecules, waters and ions can be retained from the template structure.
• It is possible to search for homologues based on the ligands they bind.
• Experimental data, especially mutagenesis is very useful when modelling ligand binding sites.
• Although the key residues may often remain, the overall structure of the protein may vary radically.
• The presence of the ligand is also likely to alter the conformation of the protein.
1ATN
1E4G
ATP Binding Site
Conformational States
• The backbone structure of the model will be almost identical to that of the template.
• Therefore the conformational state of the template will be retained in the resultant homology model.
• This is important when considering the open or closed conformation of a channel…
• … or the Apo versus bound state of a ligand binding site.
Closed
Open
Loop Modeling
Loop Modelling
Issues with Loop Modelling
• As loops are less restrained by hydrogen bonding networks they often have increased flexibility and therefore are less well defined.
• In addition the increased mobility make looped regions more difficult to structurally resolve.
• Proteins are often poorly conserved in loop regions.
• There are usually residue insertions or deletions within loops.
• Proline and Glycine resides are often found in loops – we’ll come back to this when discussing Model evaluation protocols.
Loop Modelling
• There are two main methods for modelling loops:
1. Knowledge based: A PDB search for fragments that match the sequence to be modelled (Levitt, Holm, Baker etc.).
2. Ab initio: A first principles approach to predict the fold of the loop, followed by minimisation steps.
• Many of the newer loop prediction methods use a combination of the two methods.
• These approaches are being developed into methods for computationally predicting the tertiary structure of proteins. eg Rosetta.
• But this is computationally expensive.
• Modeller creates an energy function to evaluate the loop’s quality.
• The function is then minimised by Monte Carlo (sampling), Conjugate Gradients (CG) or molecular dynamics (MD) techniques.
Loops – the Rosetta Method
Find fragments (10 per amino acid) with the same sequence and secondary structure profile as the query sequence.
Combine them using a Monte Carlo scheme to build the loop.
David Baker et al.
Predicting Sidechain Conformations
1. Networks of side chain contacts are important for retaining protein structure.
2. Sidechains may adopt a variety of different conformations, but this is dependent on the residue type.
For example a threonine generally adopts 3 conformations, whilst a lysine may adopt up to 81.
3. This is dependent backbone conformation of the residue.
4. The different residue conformations are known as rotamers.
5. Where a residue is conserved it is best to keep the side chain rotamer from the template than predict a new one.
6. Rotamer prediction accuracy is high for buried residues, but much lower for surface residues:– Side chains at the surface are more flexible.– Hydrophobic packing in the core is easier to handle than the electrostatic
interactions with water molecules. (cytoplasmic proteins)
7. Most successful method is SCWRL by Dunbrack et al.: http://dunbrack.fccc.edu/SCWRL3.php
Model Refinement
Refinement
Energy minimization Molecular dynamics
– Big errors like atom clashes can be removed, but force fields are not perfect and small errors will also be introduced – keep minimization to a minimum or matters will only get worse.
Error Recovery
If errors are introduced in the model, they normally can NOT be recovered at a later step
– The alignment can not make up for a bad choice of template.
– Loop modeling can not make up for a poor alignment.
If errors are discovered, the step where they were introduced should be redone.
Model Validation
Validation
1. Stereochemical checks on bond lengths, angles and atomic contacts Most programs will get the bond lengths and angles right
2. Ensures that the backbone conformation of the model is normal.
3. Evaluate the Ramachandran PlotThe Ramachandran plot of the model usually looks pretty much like the Ramachandran plot of the template
4. Check the inside/outside distributions of polar and apolar residues
5. Check if validate the known biological/biochemical data
– Active site residues
– Modification sites
– Interaction sites
Model Evaluation With Modeller
1. For every model, Modeller creates an objective function energy term, which is reported in the second line of the model PDB file (.B*).
This is not an absolute measure but can be used to rank models calculated from the same alignment. The lower the value the better.
2. DOPE scoring
3. A Cα-RMSD (Root Mean Standard Deviation) between the template structure and models can also be used to compare the final model to its template.
A good Cα-RMSD will be less than 2Å.
4. Modeller is good on the whole, but sometimes struggles with residues found in loops.
Ramachandran Plot
α-helix
β-strand
PsiDihedral
Angle
Phi Dihedral Angle
left-handedhelix
Peptidedihedralangles
Ramachandran Plot
• The results of the ramachandran plot will be very similar to that of the template.
• A Good template is therefore key!
• Most residues are mainly found on the left-hand side of the plot.
• Glycine is found more randomly within plot (orange), due to its small sidechain (H) preventing clashes with its backbone.
• Proline can only adopt a Phi angle of ~-60° (green) due to its sidechain.
• This also restricts the conformational space of the pre-proline residue.
N
Structure Validation
ProCheck: http://www.biochem.ucl.ac.uk/~roman/procheck/procheck.html
http://services.mbi.ucla.edu/PROCHECK/
WhatIf server :
http://swift.cmbi.kun.nl/WIWWWI/
ProQ :
http://www.sbc.su.se/~bjorn/ProQ
Biotech Validation Suite:
http://biotech.embl-ebi.ac.uk:8400/
RAMPAGE:
http://mordred.bioc.cam.ac.uk/~rapper/rampage.php
+----------<<< P R O C H E C K S U M M A R Y >>>----------+
| |
| mgirk .pdb 2.5 104 residues |
| |
*| Ramachandran plot: 91.7% core 7.6% allow 0.3% gener 0.4% disall |
| |
*| All Ramachandrans: 15 labelled residues Backbone |
*| Chi1-chi2 plots: 6 labelled residues Sidechain |
| Main-chain params: 6 better 0 inside 0 worse |
| Side-chain params: 5 better 0 inside 0 worse |
| |
*| Residue properties: Max.deviation: 16.1 Bad contacts: 10 |
*| Bond len/angle: 8.0 Morris et al class: 1 1 3 |
| |
| G-factors Dihedrals: 0.10 Covalent: 0.29 Overall: 0.16 |
| |
| M/c bond lengths: 99.1% within limits 0.9% highlighted |
*| M/c bond angles: 98.1% within limits 1.9% highlighted |
| Planar groups: 100.0% within limits 0.0% highlighted |
| |
+----------------------------------------------------------------------------+
+ May be worth investigating further. * Worth investigating further.
PROCHECK
Validation – ProQ Server
ProQ is a neural network based predictor that based on a number of structural features predicts the quality of a protein model.
ProQ is optimized to find correct models in contrast to other methods which are optimized to find native structures.
Arne Elofssons group: http://www.sbc.su.se/~bjorn/ProQ/
CASP
• Critical Assessment of Structure Prediction.
• A Biennial competition that has run since 1994.
• The next competition will be in 2008 (CASP8)
• http://predictioncenter.org/
• Its goal is to advance the methods for predicting protein structure from sequence.
• Protein structures yet to be published are used as blind targets for the prediction methods, with only sequence information released.
• Competitors may use Homology Modelling, Fold recognition or Ab Initio structural prediction methods to propose the structure of the protein.
Summary
• Homology Modelling is a valuable tool for structural biologists. It is important to take time when constructing a model.
• There are five main stages:
1. Identify an appropriate template structure(s).
2. Create a Sequence alignment.
3. Perform the homology modelling.
4. Analyse and Evaluate the quality of the model.
5. Refinement.
• Successful homology modelling depends on the following:
– Template quality
– Alignment (add biological information)
– Modelling program/procedure (use more than one)
• Always validate your final model!
Modeller: G5G8
46
Protein Modeling With Modeller: An Example
Alignment 1. Promals3d Alignment2. Generate alignment file for Modeller
Python script for running Modeller 1. Model_generation.py
Model selection 1. DOPE score2. GA341 score
Rosetta: PDZ3
48
Rosetta Protein Design (Rosetta 3.1 and up)
https://www.rosettacommons.org/
Basic procedure 1. Generate profile for the protein to be designed.
(make_fragments.sh)2. Idealize protein structure (design_ideal.sh)3. Fixed back bond design (design_fix.sh)4. Flexible back bond design (design_flex.sh)
Major parameters that control flexible backbone protein design
1. Residue Definition File2. Extended rotamer libraries : ex1, ex2, ex3
49
Rosetta Protein Design: An Example
CommandRun_1be9.bat
Residue definition file1be9.resfile
Flag fileFlag
Model selection
Blue: X-Ray
Red: 1be9_0555
Score: -223.848
Magenta: 1be9_0278
Score: -215.518
50
0
0.1
0.2
0.3
0.4
0.5
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Plot of CMR of 240 PDZ Sequences
GEEDIPREPRRIVIHRGSTGLGFNIIGGEDGEGIFISFILAGGPADLSGE
LRKGDQILSVNGVDLRNASHEQAAIALKNAGQTVTIIAQYKPEE
1 11 21 31 41
23-70
34-73
60-77
60-74
21-77
34-77
70-74
51-70
45-51
25-34
51
Distribution of Rosetta Scores
Rosetta Score Distribution
0
5
10
15
20
25
30
35
-237.5 -232.5 -227.5 -222.5 -217.5 -212.5 -207.5 -202.5 -197.5 -192.5 -187.5 -182.5 -177.5 -172.5
Rosetta Score
Perc
en
t (%
)
52
Statistical Coupling Analysis of 240 Rosetta Sequences
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
1.1
0.1
0.2
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0.4
0.5
0.6
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1
1.1
Modeling Reaction
A Hybrid QM/MM Approach
The development of hybrid QM/MM approaches is guided by the general idea that
large chemical systems may be partitioned into an electronically important region
which requires a quantum chemical treatment and a remainder which only acts in a
perturbative fashion and thus admits a classical description.
The QM/MM Modelling Approach
• Couple quantum mechanics and molecular mechanics approaches
• QM treatment of the active site
– reacting centre
– excited state processes (e.g. spectroscopy)
– problem structures (e.g. complex transition metal centre)
• Classical MM treatment of environment
– enzyme structure
– zeolite framework
– explicit solvent molecules
– bulky organometallic ligands
QM/MM Methods
• Construct a Hamiltonian for the system consisting of a QM region and an MM region
/QM MM QM MMH H H H
• QM and MM regions interact mechanically and electronically (electrostatics, polarization)
• If bonds cross boundary between QM and MM region:
– Cap bonds of QM region with link atoms
– Use frozen or hybrid orbitals to terminate QM bonds
The Simplest Hybrid QM/MM Model Hamiltonian for the molecular system in the Born-Oppenheimer approximation:
esChExternalofEffect
nuclei
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esch
k ik
kielectrons
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knuclei
ij ij
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electrons
iij
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R
QZ
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rR
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arg
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2
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The main drawbacks of this simple QM/MM model are: • it is impossible to optimize the position of the QM part relative
to the external charges because QM nuclei will collapse on the negatively charged external charges.
• some MM atoms possess no charge and so would be invisible to the QM atoms
• the van der Waals terms on the MM atoms often provide the only difference in the interactions of one atom type versus another, i.e. chloride and bromide ions both have unit negative charge and only differ in their van der Waals terms.
“Standard” QM Hamiltonian
A Hybrid QM/MM Model
So, it is quite reasonable to attribute the van der Waals parameters (as it is in the MM method) to every QM atom and the Hamiltonian describing the interaction between the QM and MM atoms can have a form:
The van der Waals term models also electronic repulsion and dispersion interactions, which do not exist between QM and MM atoms because MM atoms possess no explicit electrons.
A. Warshel, M. Levitt // Theoretical Studies of EnzymicReactions: Dielectric, Electrostatic and steric stabilization of the carbonium ion in the reaction of lysozyme. // J.Mol.Biol. 103(1976), 227-49
nuclei
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j ij
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j ij
jielectrons
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j ij
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MMQMR
B
R
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QZ
r
QH
612/ˆ
The Hybrid QM/MM Model
Now we can construct a “real” hybrid QM/MM Hamiltonian:
A “standard” MM force field can be used to determine the MMenergy. For example, AMBER-like force field has a form:
nuclei
i
atomsMM
j ij
ij
ij
ijnuclei
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atomsMM
j ij
jielectrons
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j ij
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B
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612/ˆ
MMMMQMQM HHHH ˆˆˆˆ/
nonbonded ij
ji
ij
ij
ij
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dihedralsanglesbonds
bMM
R
R
B
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An
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)()(
nuclei
ij ij
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Choice of QM method
... is a compromise between computational efficiency and practicality and the desired chemical accuracy.
The main advantage of semi-empirical QM methods is that their computational efficiency is orders of magnitude greater than either the density functional or ab initio methods
OP
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Calculation times (in time units)
1800
1
36228
1
RHF/6-31G*
PM3
Hints for running QM/MM calculations
Choosing the QM region
There are no good universal rules here
One might want to have as large a QM region as possible
However, having more than 80-100 atoms in the QM region will lead to simulations that are very expensive.
for many features of conformational analysis, a good MM force field may be better than a semi-empirical or DFTB quantum description.
Hints for running QM/MM calculationsChoosing the QM region
• At present all parts of the QM simulation are parallel except the density matrix build and the matrix diagonalisation.
• For small QM systems these two operations do not take a large percentage of time and so acceptable scaling can be seen to around 8 cpus.
• However, for large QM systems the matrix diagonalization time will dominate and so the scaling will not be as good.
Hints for running QM/MM calculationsParallel Simulations
• Boundary through space (solute (QM) + solvent (MM))1. Unpolarized interaction: Solute (QM) + Solvent (MM) 2. Polarized QM/unpolarized MM3. Fully polarized interactions
• Boundary through bondLink atoms
Two Scenarios of Hybrid QM/MM
• EVB attempts to combine empirical potential energy functions with valence bond ideas to describe chemical reactions efficiently and accurately.
• EVB starts with a NN potential energy matrix:
N diabatic states (diagonal)N (N-1) couplings (off-diagonal)
• Each diabatic state looks like a configuration in a standard non-reactive force field.
• Off-diagonal coupling elements: interaction between each diabatic state and the N-1 remaining states.
• Diagonalize V adiabatic states. The minimal value is the ground state.
Empirical valence bond: a method related to QM/MM
2
*
2
2
*
1
HOHHO
HOHHO
ba
ba
Basic Idea – to be continued
If two diabatic states …
Secular Equation
Overlap integral is neglected
2/1
2
12
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222121
121211
2221
1211
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2
1
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VVV
VVV
EVESV
ESVEV
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Amber features new and significantly improved QM/MM support
The QM/MM facility supports gas phase, implicit solvent (GB) and periodic boundary (PME) simulations
Compared to earlier versions, the QM/MM implementation offers improved accuracy, energy conservation, and performance.
Amber QM/MM
Amber QM/MM Example
Summary of MMMS Couse
MM&MS Curriculum:
https://Mulan.swmed.edu/mmms/molecular_modelling.html
Application of MMMS in Biomedical Research
• Structure refinement
• Protein function
1. Mutagenesis
2. Dynamics
• Drug design
Basic Approaches of Free Energy Calculations
Zinc Database
(20 million cpds)
HTS Docking
(autodock)
Structure Refinement
(Minimization/MD)
Promising Hits
Binding Free Energy
Prediction
(MM/PB/GB-SA)
Candidates
to bioassay
Thank You For Your Attention
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